{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/machine-learning-rationalization-and","title":"Machine-learning rationalization and prediction of solid-state synthesis conditions","arxiv_id":"2204.08151","date":"2022-04-18","proceeding":null,"authors":["Haoyan Huo","Christopher J. Bartel","Tanjin He","Amalie Trewartha","Alexander Dunn","Bin Ouyang","Anubhav Jain","Gerbrand Ceder"],"abstract":"There currently exist no quantitative methods to determine the appropriate conditions for solid-state synthesis. This not only hinders the experimental realization of novel materials but also complicates the interpretation and understanding of solid-state reaction mechanisms. Here, we demonstrate a machine-learning approach that predicts synthesis conditions using large solid-state synthesis datasets text-mined from scientific journal articles. Using feature importance ranking analysis, we discovered that optimal heating temperatures have strong correlations with the stability of precursor materials quantified using melting points and formation energies ($\\Delta G_f$, $\\Delta H_f$). In contrast, features derived from the thermodynamics of synthesis-related reactions did not directly correlate to the chosen heating temperatures. This correlation between optimal solid-state heating temperature and precursor stability extends Tamman's rule from intermetallics to oxide systems, suggesting the importance of reaction kinetics in determining synthesis conditions. Heating times are shown to be strongly correlated with the chosen experimental procedures and instrument setups, which may be indicative of human bias in the dataset. Using these predictive features, we constructed machine-learning models with good performance and general applicability to predict the conditions required to synthesize diverse chemical systems. Codes and data used in this work can be found at: https://github.com/CederGroupHub/s4.","url_abs":"https://arxiv.org/abs/2204.08151v2","url_pdf":"https://arxiv.org/pdf/2204.08151v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"machine-learning-rationalization-and","repo_url":"https://github.com/cedergrouphub/s4","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2204.08151","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.08151"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/cedergrouphub/s4","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":5},"by_repo_kind":{"official":{"samples":5,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"57a7388ebdd99d47","entry":"estimate_average_partial_importance","repo":"cedergrouphub/s4","repo_kind":"official","path":"s4/ml/nonlinear.py","file_url":"https://github.com/cedergrouphub/s4/blob/HEAD/s4/ml/nonlinear.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"57a7388ebdd99d47"}},{"code_sha256_prefix":"1e2e718ffe684196","entry":"scatter_matrix_plot","repo":"cedergrouphub/s4","repo_kind":"official","path":"s4/ml/plotting.py","file_url":"https://github.com/cedergrouphub/s4/blob/HEAD/s4/ml/plotting.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"1e2e718ffe684196"}},{"code_sha256_prefix":"4d511f3c7255ebc6","entry":"skip_7th_row","repo":"cedergrouphub/s4","repo_kind":"official","path":"s4/ml/plotting.py","file_url":"https://github.com/cedergrouphub/s4/blob/HEAD/s4/ml/plotting.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"4d511f3c7255ebc6"}},{"code_sha256_prefix":"9734421a1312e91a","entry":"unique","repo":"cedergrouphub/s4","repo_kind":"official","path":"s4/cascade/cascade_model.py","file_url":"https://github.com/cedergrouphub/s4/blob/HEAD/s4/cascade/cascade_model.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"9734421a1312e91a"}},{"code_sha256_prefix":"ab26ea6e34b01c6c","entry":"weights2colors","repo":"cedergrouphub/s4","repo_kind":"official","path":"s4/ml/utils.py","file_url":"https://github.com/cedergrouphub/s4/blob/HEAD/s4/ml/utils.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ab26ea6e34b01c6c"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}